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Record W4388288497 · doi:10.3390/app132112005

Characterization of Cardiac Fat in Atrial Fibrillation Patients Prior to Ablation Treatment

2023· article· en· W4388288497 on OpenAlexafffund
Dima Hamze, Julio García

Bibliographic record

VenueApplied Sciences · 2023
Typearticle
Languageen
FieldMedicine
TopicCardiovascular Disease and Adiposity
Canadian institutionsLibin Cardiovascular Institute of AlbertaAlberta Children's HospitalUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of CanadaSiemens HealthineersUniversity of Calgary
KeywordsMedicineInternal medicineCardiologyAtrial fibrillationEpicardial adipose tissueCardiac function curveOverweightBody mass indexObesity paradoxDiastoleAdipose tissueObesityHeart failureBlood pressure

Abstract

fetched live from OpenAlex

Epicardial adipose tissue (EAT) and pericardial adipose tissue (PAT) contribute to the development of left atrial fibrillation (AF). The purpose of this study is to determine the factors influencing cardiac fat, evaluate its impact on heart function, and evaluate its role in the recurrence of AF. Cardiac MRI exams of n = 198 patients with paroxysmal AF were retrospectively analyzed to quantify EAT and PAT. Body mass index (BMI) showed significant associations with increased EAT, PAT, and total cardiac fat, particularly with the total end-systolic area (p < 0.001). Males were associated with increased PAT (r = −0.331, p < 0.001) and EAT (r = −0.168, p = 0.019). Increased PAT end-diastolic volume was also associated with an increase in LV mass (r = 0.249, p < 0.01). An inverse relationship between EAT end-systolic area and cardiac index (r = −0.220, p < 0.01) was observed. Although BMI did not significantly affect AF recurrence, overweight patients (36%) experienced slightly more AF recurrence than obese patients (33%). Obesity is substantially associated with an increase in EAT and PAT, while sex appears to play a greater role in PAT than EAT and decreased cardiac function.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.475
Threshold uncertainty score0.169

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.017
GPT teacher head0.264
Teacher spread0.246 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2023
Admission routes2
Has abstractyes

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